Dataset 2f 100 Simulation

Same as Dataset 2f but sample size increased to 100. All different interaction matrices

1. Load Library

---

library(tidyverse)
library(igraph)
library(NBZIMM)
library(SpiecEasi)
library(LIMON)
library(here)
library(lme4)
library(Matrix)
library(tscount)
library(patchwork)
library(MASS)
library(matrixcalc)
library(gridExtra)
library(devtools)
library(miaSim)
library(reshape2)
library(corpcor)

2. Simulate Counts

Simulate a different starting covariance matrix and counts per subject. ie each one has their own seed

# Initalize an empty list
####################################################
count_tables1 <- list()

# The Loop
####################################################

# Set the indices
for (i in 1:100) {
  
  # Set the seed for each subject
  set.seed(12345 + i)  
  
  # Create Starting covariance matrix
  n <- 50^2  
  lower_values <- runif(n / 2, min = -0.8, max = -0.5)  
  upper_values <- runif(n / 2, min = 0.5, max = 0.8)
  user_interactions <- c(lower_values, upper_values)
  user_interactions <- sample(user_interactions)
  user_A  <- randomA(
    n_species = 50,
    interactions = user_interactions,
    diagonal = -1.0,
    connectance = 0.5,
    scale_off_diagonal = 0.2,
    symmetric = TRUE)
  
  # Heatmap of the covariance matrix
  color_palette <- colorRampPalette(c("red", "white", "blue"))(20)
  heatmap(
    user_A,
    Colv = NA,
    Rowv = NA,
    col = color_palette,
    zlim = c(-1, 1), 
    main = paste("Subject", i)
  )
  
  # Run the simulation
  tse_glv <- simulateGLV(n_species = 50,
                         A = user_A,
                         t_start = 0, 
                         t_store = 4,
                         stochastic = FALSE,
                         norm = FALSE,
                         error_variance = 0.1)

  # Get the count table
  sim_data <- tse_glv@assays@data@listData[["counts"]]
  
  # Store the count table in the list
  count_tables1[[i]] <- t(sim_data)
}

# Merge together
####################################################

# Combine the count tables
combined_count_table <- do.call(rbind, count_tables1)

# Add Rownames
rownames(combined_count_table) <- paste0("Sbj", rep(1:100, 
                            each = nrow(count_tables1[[1]])), "_Time", 1:4)

3. Create fake metadata

Make Metadata and merge with the count data

# Set seed
set.seed(12345)
# Df 1 is Metadata
########################################################
meta_data <-  expand.grid(Time = 1:4,ID = 1:100)
rownames(meta_data) <- rownames(combined_count_table)



# Df 2 is Metadata merged with Counts
########################################################
#Round off and increase
combined_count_table <- as.data.frame(combined_count_table + abs(min(combined_count_table)))
combined_count_table <- (combined_count_table)*10
meta_counts <- base::merge(meta_data, combined_count_table, by ="row.names", all = TRUE)
meta_counts <- column_to_rownames(meta_counts, "Row.names")

Plot the data to look at the differences among subjects

Graphs to Check

# Individual Species Plots
########################################################
# Pivot to long data
count_long <- tidyr::pivot_longer(meta_counts, cols = starts_with("sp"), names_to = "Species")

# Plot the data
count_long %>%
  ggplot(aes(x = Time, y = value, colour = as.factor(ID),
             group = as.factor(ID), linetype = as.factor(ID))) +
  geom_line() + 
  geom_point() +
  geom_jitter() +
  ylab("Count") +
  labs(linetype = "ID", color = "ID") +
  facet_wrap(~ Species) +  # Create a panel for each species
  theme(legend.position = "none") +
  ggtitle("Time Series Data")

# Plot only two
########################################################
# Pivot to long data
metacounts_filt <- meta_counts[,c(1:6)]
count_long <- tidyr::pivot_longer(metacounts_filt, cols = starts_with("sp"), names_to = "Species")

# Plot the data
count_long %>%
  ggplot(aes(x = Time, y = value, colour = as.factor(ID),
             group = as.factor(ID), linetype = as.factor(ID))) +
  geom_line() + 
  geom_point() +
  geom_jitter() +
  ylab("Count") +
  labs(linetype = "ID", color = "ID") +
  facet_wrap(~ Species) +  # Create a panel for each species
  theme_linedraw() +
  theme(axis.text.x = element_text(family = "arial",color = "black", size = 14),
        axis.text.y = element_text(family = "arial",color = "black", size = 14),
        axis.title.x = element_text(family = "arial",color = "black", size = 14),
        axis.title.y = element_text(family = "arial",color = "black", size = 14),
        legend.position = "none",
        strip.text = element_text(face = "bold", family = "arial", color = "white", size = 15))

# Distribution of counts
########################################################
hist(as.matrix(combined_count_table), breaks = 100, main = "Distribution of GLV Data", xlab = "Counts")

4. Save the data

Save the Counts

# Make more like counts
meta_counts[,3:52] <- round(meta_counts[,3:52])
write.csv(meta_counts, here("Data","GLV_SimData", "Dataset_2f", "GLV_100.csv"))

# Save just timepoint 2
meta_counts_filt <- meta_counts %>% filter(Time == 2)
write.csv(meta_counts_filt, here("Data","GLV_SimData", "Dataset_2f", "GLV_100_T2.csv"))